Feb. 12, 2025

AI and Automation's Evolution

AI and Automation's Evolution

On Beyond Confidence, Divya Parekh talks with Forrester VP Craig Le Clair, author of Random Acts of Automation, about how AI is reshaping careers, increasing workplace surveillance, and disrupting stability.

Discover the biggest risks—job...

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On Beyond Confidence, Divya Parekh talks with Forrester VP Craig Le Clair, author of Random Acts of Automation, about how AI is reshaping careers, increasing workplace surveillance, and disrupting stability.

Discover the biggest risks—job displacement, the gig economy, and more—and learn how to stay ahead. Whether you're a leader, coach, or professional, this episode is your roadmap to future-proofing your career.

Tune in now!

Beyond Confidence is broadcast live Tuesdays at 10AM ET on W4WN Radio - Women 4 Women Network (www.w4wn.com) part of Talk 4 Radio (www.talk4radio.com) on the Talk 4 Media Network (www.talk4media.com). Beyond Confidence TV Show is viewed on Talk 4 TV (www.talk4tv.com).

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WEBVTT

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The topics and opinions expressed on the following show are

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solely those of the hosts and their guests and not

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those of W four WN Radio. It's employees are affiliates.

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directed to those show hosts. Thank you for choosing W

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four WN Radio.

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This is Beyond Confidence with your host w park. Do

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you want to live a more fulfilling life? Do you

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want to live your legacy and achieve your personal, professional,

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and financial goals? Well? Coming up on dvparks Beyond Confidence,

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you will hear real stories of leaders, entrepreneurs, and achievers

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who have stepped into discomfort, shattered their status quo, and

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are living the life they want. You will learn how

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relationships are the key to achieving your aspirations and financial goals.

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Moving your career business forward does not have to happen

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at the expense of your personal or family life or

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vice versa. Learn more at www. Don't Divpork Dot com

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and you can connect with dvant contact dans dvpark dot com.

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This is beyond confidence and now here's your host div Park.

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Good morning listeners. So it's Tuesday and a fun day

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to be with you. So recently, it was very interesting.

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I was talking to one of my clients and she

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was very very upset at her boss, and so as

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we were kind of going through our coaching session, and

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I invited her to think of her boss as a

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five year old child, because she was not in a

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position to leave just immediately. And I shared with her that, like,

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you don't think about it, think of your boss as

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a five year old, and when you see him as

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a five year old, and then let's say, you know

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he's throwing ten and how do you feel about that?

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And she said that, surprisingly, I can be kind. And

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so here's the thing. Kindness is not just something that

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you do it in action. It's also how you see

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other people and how you think about them, so not

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knowing what may be going on in their lives, because

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think about it, when you are giving away the space

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in your head, you're actually giving away that primary real

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estate which is so precious. So it's not about going

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and allowing people to be mean to you, it's just

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about how you are reacting to it. So this way

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you can be kind to yourself, you can kind to

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be others. And if you're ready to step up your game,

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do get our books. And here's what I can share

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with you that it's trueways. Not only you step up

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your game, not only you help us reread the message,

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but we also help Kiva entrepreneurs by donating partial proceeds

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from our book sales. So let's bring in on our

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guests to welcome Craig.

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Well, thank you, Diva, it's great to be here. Thank

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you for having me.

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Absolutely so great. Share with us. If you're a call

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a moment or a person who left a positive mark

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on you in your childhood, are you well?

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In my in my youth, it was my probably my

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uncle Ted. He was my father passed away when I

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was quite young, and Ted kind of became my mentor

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in life, and he was just always doing projects and

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he was very optimistic. He emphasized both hands on ability

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to do things but also mental abilities to do things.

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And you know, he really was a very important influence

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in my life.

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Yeah, no, definitely, it's so important to have those mentors

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in life, because usually for kids, they emulate adults and

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tell me like, did you have any specific interest in

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your youth?

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Uh? So I I liked basketball quite a bit. I

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played all through high school, and I had some scholarships

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to Division three. Uh, you know basketball, so you know,

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colleges and so forth. I ended up going to Georgetown,

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and unfortunately when I got there, they had upgraded the

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program to Division one with Patrick Ewing and really fabulous players.

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So I never actually got to play in college. But

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I've always loved sports.

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Uh.

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And uh, you know, I've always liked I've always liked writing.

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That's something I've always I've always done. You know, one

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thing that Uncle Ted did is he discouraged me from

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going into journalism. So that's not a good field, and

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so I kind of listened to them, and you know,

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I came back to it. After working in the software

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industry for quite a bit of time, I found I was,

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you know, I wasn't really that happy, you know, being

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kind of the entrepreneur and the business guy. So I

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became a what's called an industry analyst, which is sort

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of a journalist, but it's a journalist that only covers

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the technology industry, and I've been doing that for eighteen

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years and really the second part of my career has

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been the happiest because I finally found you know, the

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area that you know, really you know, gave me passion

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in my work life.

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Yeah, so tell us what led you to the software.

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Well, I was originally an economist, and I was working

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on government contracts in Washington, d C. Where I stayed

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after college and graduate school, and I just you know,

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I was working for a big company and all of

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a sudden, there were no economic projects, and so they

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put me on a technology project to uh try to

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forecast the next generation of data communications. And I was

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looking at it and yeah, it's kind of an interesting story.

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I was like very junior. I knew nothing about technology,

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but I was with in an old key punch room

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with all these sort of revolving door consultants that had

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filtered in from other from their from their federal work,

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and uh, no one knew what to do, and I

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started just writing. All of a sudden, I was kind

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of leading the project and I was as I was

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looking at the data communications world. And that was early

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we're talking, you know, ancient history now, uh, you know,

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it just struck me that boy, there's something here, this

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might be a good place to learn about. And then

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I just became more and more technical as uh, you know,

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as I progressed. Yeah.

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Absolutely, So did you just kind of stay in the

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federal area or did you also work in the private industry? No.

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I I grew weary of the federal contracting area because

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I just felt you were never really making any any

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really strong progress. You know. So, as many things happened,

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I met a girl at a bar, I went up

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to Boston, where I live now, and I started working

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in a private sector at technology companies, which I did

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for a number of years. Yeah.

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Yeah, So you mentioned that you know about federal contract

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that it didn't go anywhere. So what would you say

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was the primary difference between the two Because in a

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lot of people are our listeners are thinking, you know,

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what area to go for, like especially the youngsters, they're

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deciding and they may have different options. So if many

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were to choose, what would you say at the pros

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and cons of both industry?

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Well, so, you know, you see what's happening right now, right,

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you know. So when I was working out for this

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company called Miner Corporation, which is a nonprofit think tank,

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and it did but basically it kind of tried to

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keep the contractors honest and and sort of protecting the

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federal government, uh in a lot of ways. But it

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was a fascinating place. But I wanted to work on

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public policy, you know, and you had to shift in administrations, uh,

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with with Reagan coming in all of a sudden, it

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was all defense. So you have these shifts that go

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with the political wins. That makes it adds a variability

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to your you know, to your job prospects. Uh. And

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it's still more dramaticly evident than what's been happening the

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last few weeks, right uh with with you know, the

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organization coming in and you know, a new leadership coming

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in and saying, you know what, we don't need all

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and we don't need all these federal jobs. Uh. And

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you know, this is a lot of great work that's

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been done for years by by a lot of people.

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So we'll see how that that shakes out. So that's

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kind of the downside, you know, on the on the

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private sector side, it's not you know, a picnic there either,

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because you have much less loyalty from companies to employees,

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and I write about that in my book Random Acts

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that you know you no longer can you count on.

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And there's so many mergers and acquisitions, you know, there's

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so much globalization. Still, you have technology and automation coming in,

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which is a a big piece of what I write

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about that are you know, going to affect and shift

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jobs around pretty dramatically. So there's really no you know,

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perfect place. But I would say that, you know, the

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private sector does give you ultimately more flexibility in terms

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of job repositioning than the federal government.

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Mm hmm. And definitely that makes sense. So as you

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continued in your career, you mentioned that, you know, you

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became a broadcaster, you became a technical person analyzing trends

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and predicting the wins of tomorrow. So if you can

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give are so many times what happens is that people think, like, oh,

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open AI is here, like l l MS, which is

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the large is it like large uge models?

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Yes, and now they're talking about small language models.

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Yeah, learnning language models are here, and then there's you know,

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all the processing and all of that. And yet if

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we kind of take a look at it, that automation

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and AI has been part of our lives for a

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long time. So if you can just kind of share

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with us the transition, that'll be great for those who

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don't know.

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Yeah, I mean we're not we're not following it narrow a,

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which is what you're referring to. You know, we've had

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machine learning that's been doing predictive analytics for a long time,

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you know, twenty years probably in various applications, but this

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generative AI large language models is very much more powerful.

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So you know, basically by training these models on the

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entire Internet and all that activity, you're able to get

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very very close to what I call in the book

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human capable automation or HCA. So you're starting to see

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a AI able to make the same kind of connections

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that a human makes. You know, think of it this way.

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You know you probably don't read your insurance policies very much. Yeah,

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I certainly don't. You know, usually there's like two pages

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of things they cover in one hundred pages of things

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they don't right, written by lawyers for other lawyers to read.

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So these models can actually allow you to have a

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conversation with a set of data like that, So you

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can actually have an AI interface to that and and

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you know ask so, you know, how long have I

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been covered? How much am I paying for this policy?

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Well?

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My errors actually get you know, it's a life insurance.

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So there's this whole new yeah, you know power that

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that comes from this ability to uh to make connections

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in a way that that human that that that humans do. Uh.

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And you know we're at the cusp of that now,

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in the early part of that. And these models are

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getting better and stronger and more capable all the time.

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So at some point, you know you're going to have

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what was called a g I artificial general intelligence, which

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is you know, uh, that's that's probably some people say

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at Forrester By the way, I work at Forrester Research,

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which is a big research company. You know, we're predicting

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that's going to know two three years away, we'll have

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a g I in the workplace. So this is a

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fun fundamentally different from that narrow AI that we've been

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using for quite a bit of time.

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Right And as we are talking about a g I,

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I know, I'm jumping into something more complex and will

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kind of come back to the original one. Now, there

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are so many talks about having the retrieval augmented generation,

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and Intel is already pretty big into implementing and a

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lot of other companies are. So can you tell for

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our listeners, you know, just the basic difference in machine learning,

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data sciences AGI and the ritual augmented generation from their

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perspective as to how is this going to impact them?

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Yeah, and that's exactly what you know. This this book

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is attempting to do. So I explain, I explain the

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relevant technology, but I do it in the context of

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four different distinct worker categories. So what's relevant for your

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audience is is not in general what the technology does,

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but how would affect the job role that they're in.

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You know, So if they're a human touch worker, you know,

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which might be you know, helping in a healthcare environment

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or even working in a warehouse, it's a very different

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set of technology that that's you know, real robotics that's

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going to affect those jobs. And I think very positively

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you're going to have you know, android robotics, You're going

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to have janitorial robotic robotics. You're going to have a

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lot of uh, physical automation that's going to make their

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jobs a lot safer and a lot less dirty and

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a lot better and there's enough you know, a need

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for human agility that uh, you'll see those jobs actually

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get paid better and be and be safer. So that's

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very very positive. If you're in the middle, you know,

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there there you have more problems because the type of

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lower level cognitive work that's done by middle jobs is

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perfect for software robots, you know. And and one of

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the my book before this was called Invisible Robots. In

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the Quiet of the Night, I was just writing about

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these RPA bots that were operating in the cloud that

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were able to come in and take over a desktop

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and do what Harry was doing for eight minutes, you know.

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And if you had one hundred Harries doing the same

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eight minute task, you know, maybe a thousand times a month,

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you could build a bot that would actually take those

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hours out. And now these are visual robots when you

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add gen ai to them, are able to even do more.

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So the middle is going to get, you know, hollowed

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out much more. And of course it's suffered from globalization

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over time, and it's already suffered from automation. So the

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path for the middle is different. And that's why I've

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separated into these four categories. The path for the middle

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is to become more of an AI operator and how

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do you use AI actually start doing jobs for those

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that have higher levels of credentials, more professional credentials, because

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AI really augments what you can do and allow you

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to be the parallee who can now do much more

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of what a lawyer does. We've already seen that with

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CNAs and nurse practitioners that are using that will use

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automation to make more planning and basically be more capable,

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so they'll get more and more value going. So there's

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a positive path for the middle as well, but it's

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the numbers don't work out as well as they do

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for the human touchworkers and of course the digital elite,

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which are the top category. That's probably everybody on your

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on your ball here. You know, actually you have a

279
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very good, uh diverse demographic for your shows, which is

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I applaud the digital league are going to suffer a

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lot from gen AI doing more of those human connections

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that that that's part of their their their job. So

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data science you mentioned, you know that applies to all

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of this. You know, it's all about the data that

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you expose to the models, and you mentioned rag you know,

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RAG is the sort of retrieval method to expose the

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right data. So think of this way. This you know,

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this amazing foundational model that's trained on the entire Internet, right,

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but you to make it work for companies, it's got

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to go and get all the enterprise data that the

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company has that's within their firewall or on their private cloud.

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And that's what RAG is doing. You know, RAG is

293
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going in saying, you know what, I'm going to retrieve

294
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this PDFs that's in this repository that are your standard

295
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operating procedures. Yeah, and I'm going to retrieve that and

296
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augment it somehow a lot of fancy terms like vector

297
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databases and so forth, but basically, I'm going to prepare

298
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it for the MOOB. So now it knows your specific

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enterprise context and it can create the work patterns that

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are needed to get something done. You know, and that's

301
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where the you know, the genius of all this is that,

302
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you know, all the automation we have today. You know,

303
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someone some you know, big integrator or software developer had

304
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to write very specific, prescriptive, step by step automation to

305
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get something done. And now you just feed this information

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into a model, and the model figures out the steps

307
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that are needed. So it's going to be very disruptive

308
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to the whole software industry because a lot of the

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business logic that's in those rule based systems will be displaced.

310
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Buy these what are called AI agents or AI models

311
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that are going to take that away. Is that too

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much detail for your own answer? No?

313
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No, no, It's like I have been playing in this

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area for quite some time. So it's important for people

315
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to understand that we have had these systems and how

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as you mentioned that it is getting ahead of it.

317
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It is about developing those metacognitive skills to training AI

318
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not to think for you, but to think with you.

319
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So this way what happened because it cannot read your mind,

320
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and EI is as good as the context and the

321
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role and the input that you're providing.

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So exactly there is.

323
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A whole and the key is like you know, dispursing

324
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the fear that people are feeling. So people are you know,

325
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there are a lot of professionals, like even software engineers who

326
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say like, oh, you know, there is a no code

327
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like you know, especially Claude Anthropic. It is really good

328
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for code generation and you can kind of drop it

329
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in there and it will generate the whole code for you.

330
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So what would be your message to them, especially with

331
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the mindset, because what happens is human beings get in

332
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their own way.

333
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Mm. Yeah, So that's an interesting one because personally, I

334
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think that the you know, uh, you know, it used

335
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to be, you know, five seven, eight years ago the

336
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advice to you know, people coming out of high school

337
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or even college was learned to code, you know, and

338
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now and now clearly that's not the right path. But

339
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I think that the idea that what's not as well understood,

340
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I think is that the actual coding part of a

341
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so for development is only about thirty percent of it.

342
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So most of the time that a software developer spends

343
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is trying to understand what the heck the business wants

344
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or the requirements, you know, understanding the testing patterns, you know,

345
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trying to break down a process, you know, decompose it

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and figure out what should be automated. Those are all

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things that are done in that what we call the SDLC,

348
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the software development life cycle. So you are going to

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have a strong augmentation for that thirty percent, but it's

350
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not going to completely disrupt or eliminate any of those jobs.

351
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You know, my my advice to the you know, you know,

352
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we we we use this term human in the loop, uh,

353
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you know quite a bit. And you know, the developer

354
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will have a model in the loop you know, to

355
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help them, and yeah, they'll spend a lot less time

356
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figuring out you know what, what little specific things that

357
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I do that make a problem. You know, they'll be

358
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able to have, you know, a a wireframe of the

359
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software you built by the model and then they'll take

360
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it from there. So it's going to reduce you know,

361
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a lot of their hours. But I think what they

362
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have to do is is become stronger on the process

363
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understanding side, on that decomposition side, on how to curate

364
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the data properly. What data is important? To your point,

365
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it's it's about the data on the way in there's

366
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going to be there's so much automation that's being planned.

367
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That's really why I wrote the book. I mean, I'm

368
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in the techno. Uh you know, I'm talking to business

369
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leaders that are just frantic to apply AI to everything.

370
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I'm talking to the tech vendors you know, who just

371
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can't wait, you know, to you know, there are four

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hundred startups in just in building AI agents, which are

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essentially digital workers.

374
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You know, you're absolutely right, And if you have anybody

375
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has had a chance to play with cha GPPRO, it

376
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will go like and they've just recently released the operators

377
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and they will go inside your computer. They'll take over

378
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your computer, book appointments for your book, your travels for you.

379
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So it's already here and the key is, like, you know,

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I want to come back to what you said is

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knowing the process, and the key to know is that

382
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human intervention is going to be there. It's going to

383
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be doing your grunt work. So it's the whole idea

384
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is even for the companies, is to take care of

385
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that repetitive work, that grunt and the grind for the

386
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people and allowing them to think, opening up the room

387
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for more curiosity and critical thinking. And then that happens

388
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when we create that intersection where humans are doing the thinking. Yes,

389
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I know, CHGBT pro and now deep seek can do

390
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connect the dots and all that. And yet that human

391
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experience that you're bringing is going to be more and

392
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more valuable based on what you're saying. So am I

393
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hearing it right?

394
00:23:01.920 --> 00:23:04.920
Yeah? No, you're you're hearing it right. I'd probably make

395
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a few a few edits to that, but I think

396
00:23:08.319 --> 00:23:11.039
it's right. I think there's going to be much greater

397
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premium on the human element or the accent. I think

398
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for a lot of companies, I like to say, the

399
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you know, the the margin is and the memories. You know,

400
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if you can create a human experience as part of

401
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your customer experience, that's going to be more more memorable.

402
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Uh.

403
00:23:30.680 --> 00:23:33.400
And you know, right now I have a chapter on

404
00:23:33.559 --> 00:23:37.680
on called, uh, you know, self service Hell, because we've

405
00:23:37.920 --> 00:23:41.799
we've really gone almost too far and forcing people to

406
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become you know, unpaid employees on the first day of

407
00:23:44.359 --> 00:23:47.400
the job and doing self service for you know, for

408
00:23:47.519 --> 00:23:51.799
various retail operations and others, and you know, we're we're

409
00:23:51.839 --> 00:23:56.160
about reaching that tipping point where clickage and where you know,

410
00:23:57.119 --> 00:24:01.000
people are just getting really frustrated. Spain has a uh

411
00:24:01.119 --> 00:24:04.680
now finds companies if they don't get a human on

412
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the phone for a certain period of time. So I

413
00:24:08.720 --> 00:24:11.640
think there's going to be a smart companies will use

414
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humans in in in more innovative ways. The one thing

415
00:24:17.000 --> 00:24:19.880
I would headed to what you said, though, is, you know,

416
00:24:19.920 --> 00:24:21.799
one of the terms that has seeped into the tech

417
00:24:21.799 --> 00:24:25.079
industry and in the you know, in general faster than

418
00:24:25.079 --> 00:24:29.839
any word I've ever seen is agentic agentic AI. Uh

419
00:24:29.880 --> 00:24:33.480
And and this is basically, you know, the idea that

420
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you have the model and full control of the process

421
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and and and implementing and making various agents that are

422
00:24:43.000 --> 00:24:46.359
built to do various things coordinate in kind of an

423
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any flow kind of way. Uh And right now there's

424
00:24:50.319 --> 00:24:56.799
no truly agentic applications that that we can find. I

425
00:24:56.839 --> 00:25:00.480
have a report coming out called, you know, the gentic

426
00:25:00.480 --> 00:25:05.680
agents are aware siting, but we're we're close to that.

427
00:25:05.799 --> 00:25:08.839
The frameworks, some of them that you've mentioned that are

428
00:25:08.839 --> 00:25:12.200
coming into play, the AGENTA AAI frameworks are going to

429
00:25:12.200 --> 00:25:15.480
allow us to build very very complex systems of that

430
00:25:15.559 --> 00:25:19.440
app that are self optimizing, self adapting, that are a

431
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complete control of a process. And for the business to

432
00:25:22.880 --> 00:25:28.599
make that investment, they're going to need to replace a human. Yeah.

433
00:25:28.680 --> 00:25:30.920
In other words, a lot of these copilots that you

434
00:25:31.000 --> 00:25:34.599
hear about. Agent Force was on you know from Salesforce

435
00:25:34.680 --> 00:25:38.160
was a Super Bowl commercial, right, you know, they're not

436
00:25:38.240 --> 00:25:40.759
making any money for any of those companies. They're not

437
00:25:40.759 --> 00:25:44.160
being monetized because putting a model in front of you,

438
00:25:44.279 --> 00:25:48.000
Diva and making you a little smarter is you know,

439
00:25:48.079 --> 00:25:50.200
you're you're probably not going to pay three hundred dollars

440
00:25:50.240 --> 00:25:54.440
a month for that, right, But when you add when

441
00:25:54.279 --> 00:25:59.480
when when you add a strong action component to it,

442
00:25:59.519 --> 00:26:01.839
where you have this sort of multi flow or any

443
00:26:01.880 --> 00:26:06.599
flow coordination of agents, you're starting to companies will pay

444
00:26:06.599 --> 00:26:09.680
three thousand dollars a month if they can replace Harry

445
00:26:10.480 --> 00:26:13.640
And that's what they're that's what they're doing. So while

446
00:26:14.079 --> 00:26:15.799
a lot of the narrative of the tech industry, I

447
00:26:15.880 --> 00:26:17.640
talked to tech beners all the time, I say, oh, well,

448
00:26:17.640 --> 00:26:19.200
there's going to be more there's going to be more

449
00:26:19.200 --> 00:26:21.119
interesting people to do. They're going to be more strategic

450
00:26:21.200 --> 00:26:24.279
and more planning. The reality is the numbers just don't work.

451
00:26:24.839 --> 00:26:27.799
They just are not enough of those kind of jobs

452
00:26:27.920 --> 00:26:32.480
to really all set. The numbers will be taken away

453
00:26:32.839 --> 00:26:35.279
by AI doing a lot of these things.

454
00:26:36.720 --> 00:26:40.480
So we've touched upon a lot of different points, and

455
00:26:41.480 --> 00:26:44.559
so what would be the best path forward for our audience.

456
00:26:46.559 --> 00:26:52.559
Well, you know, I think if you're on your daughter

457
00:26:52.640 --> 00:26:55.319
is in the basement playing video games for six months

458
00:26:55.440 --> 00:26:57.119
or so, I think you go out and buy my

459
00:26:57.160 --> 00:27:04.160
book MAXI of Automation, Say go get a job. I

460
00:27:05.279 --> 00:27:09.319
really do think that, you know, there has to be

461
00:27:09.359 --> 00:27:14.359
a better understanding of the automation trends that are occurring

462
00:27:14.440 --> 00:27:18.559
and how they affect the job that you're in. And

463
00:27:19.119 --> 00:27:22.480
you know, I interviewed you know, one hundred uh and

464
00:27:22.559 --> 00:27:25.839
I you know, probably have written out forty different workers

465
00:27:25.880 --> 00:27:28.440
and I listened to their stories, you know, I have,

466
00:27:28.640 --> 00:27:31.680
you know, and so the machinist who was a top

467
00:27:31.680 --> 00:27:36.319
performer for twenty years, and you know, they upgraded his machine,

468
00:27:36.599 --> 00:27:39.599
you know, so that now instead of doing the machine

469
00:27:39.599 --> 00:27:43.319
operation which he was top performer, he had to enter

470
00:27:43.559 --> 00:27:46.400
you know, G codes you know, into the machine. Basically

471
00:27:46.440 --> 00:27:48.480
the setup had gotten much more complicated. And he went

472
00:27:48.599 --> 00:27:50.880
was boss and said, you know, I just can't do

473
00:27:50.960 --> 00:27:53.480
this anymore, you know, you know, can I just run

474
00:27:53.480 --> 00:27:55.759
the machine and somebody else can? You know, and some

475
00:27:56.559 --> 00:27:59.880
you know, a young guy tried to help him train him.

476
00:28:00.519 --> 00:28:03.720
You know, you talk to you know, I talked to

477
00:28:03.720 --> 00:28:06.279
this woman named Julie in a big call center and

478
00:28:06.319 --> 00:28:10.440
she's having to train her software robots really to do

479
00:28:10.559 --> 00:28:13.079
all of her updates and all her all her work.

480
00:28:13.759 --> 00:28:15.359
You can kind of see, you know, to be genius

481
00:28:15.359 --> 00:28:17.920
to see where this is going. So for each of

482
00:28:17.960 --> 00:28:21.519
those kind of roles, you know, there are steps you

483
00:28:21.559 --> 00:28:24.720
can take and that are very positive and a lot

484
00:28:24.799 --> 00:28:26.519
of it is kind of the things you start to

485
00:28:26.519 --> 00:28:28.640
talk about in the beginning of the show. It's about

486
00:28:29.480 --> 00:28:34.000
constructive ambition and correct and you know, there are a

487
00:28:34.000 --> 00:28:37.400
lot of specifics. Some of the things that you look

488
00:28:37.440 --> 00:28:40.200
at when you look at upgrading your skills are just overwhelming.

489
00:28:40.720 --> 00:28:45.119
But take an incremental approach, set your goals modestly so

490
00:28:45.200 --> 00:28:49.240
you can have strong, you know, achievement. An area that

491
00:28:49.279 --> 00:28:52.440
I'm I'm fascinated with is critical thinking, you know, which

492
00:28:52.480 --> 00:28:54.640
is you know, the reason we're all here today is

493
00:28:54.680 --> 00:28:57.599
because there was a you know, a Russian who you know,

494
00:28:57.720 --> 00:29:01.279
had gotten the nuclear codes that the US was attacking

495
00:29:01.400 --> 00:29:05.279
with nuclear with five nuclear missiles, and he didn't pull

496
00:29:05.359 --> 00:29:09.160
the trigger because he noticed that there were some anomalies.

497
00:29:09.200 --> 00:29:14.759
He applied critical thinking before he basically counter the the UH.

498
00:29:14.960 --> 00:29:17.640
You know, there's probably a very little known guy you

499
00:29:17.680 --> 00:29:21.359
know that saved us uh and he is all critical thinking.

500
00:29:21.920 --> 00:29:24.119
So one of the things about critical thinking is that

501
00:29:24.200 --> 00:29:27.839
it it's not just for the digital elite, which is

502
00:29:27.880 --> 00:29:31.079
a lot how it started. It's not just for you know,

503
00:29:31.160 --> 00:29:33.440
you know people with the more advanced credentials. It can

504
00:29:33.480 --> 00:29:36.480
be applied to anyone working in a warehouse or so forth.

505
00:29:36.880 --> 00:29:39.759
And now there's a lot of information to make it

506
00:29:39.799 --> 00:29:43.359
more adaptable to and more understandable and less academic and

507
00:29:43.359 --> 00:29:46.960
its approach. I really like that aspect of it. I

508
00:29:46.960 --> 00:29:49.599
think this idea of being an AI operator, you know,

509
00:29:49.680 --> 00:29:53.079
for for those that are working in the in the

510
00:29:53.519 --> 00:29:56.680
human touch area. A lot of kids today have great

511
00:29:56.720 --> 00:30:00.119
device skills. They want to work on their phones, they

512
00:30:00.160 --> 00:30:02.079
live on their phones, and there are a lot of

513
00:30:02.160 --> 00:30:06.680
jobs that want those skills. So you know, why work

514
00:30:06.680 --> 00:30:09.759
in McDonald's. You know, when when you can get a

515
00:30:09.839 --> 00:30:12.279
job that can leverage skills that you have, have a

516
00:30:12.359 --> 00:30:17.640
much better job, you know, the the human touch workers.

517
00:30:18.000 --> 00:30:20.559
There are certain industries that are applying automation in ways

518
00:30:20.559 --> 00:30:24.400
that benefit them, you know, such as security with with

519
00:30:24.519 --> 00:30:27.559
the android, you know, robotic officers. That's a great area

520
00:30:27.599 --> 00:30:32.400
to go into. It's much better than food service, you know.

521
00:30:32.480 --> 00:30:35.519
So that's the kind of advice that I that I get.

522
00:30:35.839 --> 00:30:38.960
You have to look at the way companies are trending

523
00:30:39.000 --> 00:30:42.359
and investing in automation and how that can make your

524
00:30:42.400 --> 00:30:45.279
job either better or worse. And and there's some cases

525
00:30:45.279 --> 00:30:48.119
that can make your job a lot better. I love

526
00:30:48.160 --> 00:30:52.880
the path that the middle has by leveraging AI to

527
00:30:53.160 --> 00:30:57.839
do the work more educated and credential you know, professional workers,

528
00:30:58.319 --> 00:31:00.680
you're definitely going to see that erosion. You know, so

529
00:31:00.720 --> 00:31:03.480
the digital leader are going to see erosion from the bottom,

530
00:31:03.680 --> 00:31:06.240
and they're going to see erosion directly, you know, by

531
00:31:06.960 --> 00:31:10.079
by jen Ai and and Ai doing more of what

532
00:31:10.119 --> 00:31:13.759
they do. But and then there's the work life pioneers,

533
00:31:13.759 --> 00:31:16.720
which is the fourth category worker. I think there's a

534
00:31:17.480 --> 00:31:19.359
you know, it's been trending for a while, right if

535
00:31:19.359 --> 00:31:21.839
you look at the number of so called gig workers

536
00:31:21.960 --> 00:31:24.720
or contract workers, it's thirty percent. It's been growing, you know,

537
00:31:24.720 --> 00:31:28.119
it's been ticking up, and it really accelerated after the pandemic.

538
00:31:29.440 --> 00:31:32.680
And the pandemic like the sequoia trees that they don't have,

539
00:31:32.759 --> 00:31:36.720
the the you know, the cones that only only surface

540
00:31:36.759 --> 00:31:40.079
after a big devastating fire, and the pandemic really opened

541
00:31:40.079 --> 00:31:43.160
the door, you know, opened the door to a lot

542
00:31:43.160 --> 00:31:45.240
of people, you know, just questioning what they're doing in

543
00:31:45.279 --> 00:31:48.519
their lives and I have some really good stories of

544
00:31:49.480 --> 00:31:52.599
you know, like a David White, who you know, adopted

545
00:31:52.599 --> 00:31:56.119
a child and just changed his life, and he became

546
00:31:56.200 --> 00:32:00.119
a foster care advocate, you know, a think tankler foster care.

547
00:32:00.000 --> 00:32:03.279
They noticed that all the foster care kids, they moved

548
00:32:03.279 --> 00:32:05.319
three or four times in there in their early years,

549
00:32:05.359 --> 00:32:08.000
and they always put their belongings in a garbage band.

550
00:32:08.960 --> 00:32:11.279
So he got his first act was to get all

551
00:32:11.319 --> 00:32:15.880
the luggage companies to donate luggage to the foster care community,

552
00:32:17.559 --> 00:32:19.640
you know. And there's a lot of companies that are

553
00:32:19.640 --> 00:32:23.960
just focusing on certification and education. That is very practical.

554
00:32:24.759 --> 00:32:28.559
You know, the government's not going to solve the skills problem.

555
00:32:28.599 --> 00:32:31.000
The skills problem is going to be solved more by

556
00:32:31.039 --> 00:32:35.119
the private sector. And even a Walmart, who is you know,

557
00:32:35.039 --> 00:32:38.000
you beat up a lot for all kinds of things.

558
00:32:38.359 --> 00:32:42.119
They have, like a hundred Walmart universities, which are interestingly

559
00:32:42.160 --> 00:32:45.400
in old warehouses that you know, are because of analytics

560
00:32:45.400 --> 00:32:47.359
and supply chain are now vacant. So they set them

561
00:32:47.440 --> 00:32:50.480
up as universities. And even in one woman, you know,

562
00:32:50.519 --> 00:32:54.960
and she'd never graduated from anything in life, so they

563
00:32:54.960 --> 00:32:59.119
have a formal graduation ceremony with blue gowns and caps,

564
00:32:59.200 --> 00:33:01.559
and she said, you know, or my kids to see

565
00:33:01.599 --> 00:33:04.640
me graduating. You know, let's just say, you know. It

566
00:33:04.680 --> 00:33:07.519
was a heart wrenching moment. So there are companies that

567
00:33:07.559 --> 00:33:14.319
are providing certifications and skills advancements that I think everyone

568
00:33:14.319 --> 00:33:15.119
can take advantage of.

569
00:33:16.359 --> 00:33:21.359
Yeah, No, that is fantastic, and it's just stepping into it,

570
00:33:21.519 --> 00:33:24.920
leaning into it because this can have an impact on

571
00:33:25.000 --> 00:33:27.839
mental health. And then of course getting your book. So

572
00:33:27.920 --> 00:33:31.519
tell us a little bit about your book and where

573
00:33:31.559 --> 00:33:34.119
can people find you and find your book.

574
00:33:34.880 --> 00:33:37.319
Well, I'd love to thank you so much again, thanks

575
00:33:37.359 --> 00:33:40.519
for having me on. Well, Lindie mentioned mental health. I have.

576
00:33:41.079 --> 00:33:44.319
The two main things about the book are specific skills

577
00:33:44.319 --> 00:33:47.960
to help workers in these four categories, starting with the

578
00:33:48.000 --> 00:33:50.240
trends affecting them from automation, and then what they can

579
00:33:50.319 --> 00:33:52.960
do about it. But it's not written for academics. It's

580
00:33:53.000 --> 00:33:55.039
not written for the business to make more money with

581
00:33:55.119 --> 00:33:58.200
technology for workers. So that's the first main thing. The

582
00:33:58.240 --> 00:34:00.880
middle section, I have a whole section with three chapters

583
00:34:00.920 --> 00:34:04.759
on mental health and I'm not in any way trained

584
00:34:06.799 --> 00:34:10.559
in that regard. I speak only to the mental health

585
00:34:10.599 --> 00:34:14.400
effects of automation. Yeah, and I have a chapter called

586
00:34:15.000 --> 00:34:18.039
invasion in surveys that we did, and I have a

587
00:34:18.079 --> 00:34:21.280
lot of data from Forester Research here to support some

588
00:34:21.320 --> 00:34:23.480
of the ideas. I'm not overwhelming the reader with a

589
00:34:23.480 --> 00:34:26.199
lot of data. But you know, when you look at

590
00:34:26.199 --> 00:34:31.400
the Random Acts of Automation, which is actually a site,

591
00:34:31.440 --> 00:34:33.760
public site that people can if they have a bad

592
00:34:33.800 --> 00:34:36.360
experience with automation, they can throw it into this site

593
00:34:36.400 --> 00:34:38.960
and then they track them and they're thousands and thousands

594
00:34:39.000 --> 00:34:43.159
of thousands. So I took the database and sorted it,

595
00:34:43.239 --> 00:34:47.039
and it turned out that of the three trust issues,

596
00:34:48.239 --> 00:34:52.199
invasion issues, and what I called isolation issues, that invasion

597
00:34:52.639 --> 00:34:56.440
was number one by about forty percent. Of all negative

598
00:34:56.719 --> 00:35:01.400
reported effects due to automation were invasive monitoring in base

599
00:35:01.480 --> 00:35:05.880
of use of biometrics, you know, you know, I have

600
00:35:06.360 --> 00:35:07.159
I have stories.

601
00:35:07.400 --> 00:35:10.840
There's a you know, the the company that owns you know,

602
00:35:10.880 --> 00:35:14.480
Madison Square Garden, they have a biometric for coming into

603
00:35:14.480 --> 00:35:19.800
their events, and you know, they basically if you were

604
00:35:19.880 --> 00:35:24.239
involved in any lawsuit against this entity, which gets sued

605
00:35:24.280 --> 00:35:26.719
all the time, you were a block.

606
00:35:26.960 --> 00:35:29.280
You know. So there was some woman with her daughter

607
00:35:29.360 --> 00:35:34.199
going to see the Roquettes one, you know, December and

608
00:35:34.719 --> 00:35:36.960
she never even heard of the company, but worked for

609
00:35:37.000 --> 00:35:39.480
a law firm that actually had some litigation with them.

610
00:35:39.559 --> 00:35:42.039
She was the bight access. I mean this is you know,

611
00:35:42.079 --> 00:35:46.119
I talked to McDonald's employee that they do you know,

612
00:35:46.559 --> 00:35:51.840
analytics on every single store mcconels real time, and if

613
00:35:51.840 --> 00:35:55.920
they're forecasting that there'll be a slowness between say ten

614
00:35:56.119 --> 00:36:00.840
and twelve o'clock at a particular store, they'll send a

615
00:36:00.880 --> 00:36:03.639
text to an employee who might be on a bus

616
00:36:03.679 --> 00:36:05.159
to come to work and tell them not to come,

617
00:36:05.360 --> 00:36:07.880
not to show up. Now, New York just passed a

618
00:36:07.960 --> 00:36:14.519
law to prevent that all of these you know, this invasive,

619
00:36:14.639 --> 00:36:17.119
all of this automation. There's a woman in Australia that

620
00:36:17.599 --> 00:36:20.119
one of the biggest insurance company in Australia that they

621
00:36:20.119 --> 00:36:25.760
put listening software on our desktop and they tracked every

622
00:36:25.880 --> 00:36:30.239
mouse movement, every time spent, you know, everything, And that

623
00:36:30.360 --> 00:36:33.239
is something that's out loud in Germany, but that's something

624
00:36:33.280 --> 00:36:35.280
that's creeping in. It's a whole area called you know,

625
00:36:35.360 --> 00:36:41.440
task mining. But you know, they fired her and they

626
00:36:41.480 --> 00:36:43.960
had this report of how many days she left a

627
00:36:43.960 --> 00:36:45.880
little bit early, how many times she did this, how

628
00:36:45.880 --> 00:36:48.840
many of that which she refuted. She said, that's not accurate,

629
00:36:50.039 --> 00:36:51.960
but she had no knowledge that they were doing this

630
00:36:52.079 --> 00:36:54.639
kind of monitoring, you know, on her. So anyway, I

631
00:36:54.679 --> 00:36:57.239
could go on and on, but this is why I

632
00:36:57.239 --> 00:37:00.679
wanted to. This type of monitoring really affect the mental

633
00:37:00.719 --> 00:37:05.719
stability and health of employees. And of course trust issues

634
00:37:05.760 --> 00:37:08.599
are are just huge. Now you can't trust almost anything

635
00:37:08.639 --> 00:37:11.199
you see on the internet except for this program, right,

636
00:37:11.599 --> 00:37:14.519
But you know, so so you.

637
00:37:14.480 --> 00:37:18.360
Know, you're bringing you know, live people, so right, a.

638
00:37:18.360 --> 00:37:21.599
Lot of people, so basically you know, and and the

639
00:37:21.639 --> 00:37:25.760
isolation aspect. I think one of the aspects of a

640
00:37:25.760 --> 00:37:28.079
lot of their work is that the digital leader not

641
00:37:28.159 --> 00:37:32.480
interacting with with mintal workers or human touch workers as much.

642
00:37:33.559 --> 00:37:35.440
You know, and we tend to be, you know, so

643
00:37:36.559 --> 00:37:39.239
digitally focused because now we're not just on our phones

644
00:37:39.559 --> 00:37:41.840
at work. I mean we're we're staring at screens. A

645
00:37:41.840 --> 00:37:43.960
lot of us are staring at screens during work all

646
00:37:44.000 --> 00:37:48.000
the time. So there's a lot of this. The whole

647
00:37:48.039 --> 00:37:51.280
aspect of random acts is that we have this juggernaut

648
00:37:51.280 --> 00:37:55.760
tech industry that's that's bursting with enthusiasm, innovation and money

649
00:37:56.079 --> 00:37:58.760
to build the next thing. But they don't know what

650
00:37:58.800 --> 00:38:02.559
this next thing will do. It's unpredictable because inherently all

651
00:38:02.599 --> 00:38:07.360
of this is based on probability. It's it's not different

652
00:38:07.400 --> 00:38:09.840
than going to Las Vegas and playing the craft tables.

653
00:38:10.360 --> 00:38:13.519
You know, you have you have statistics that are taking

654
00:38:13.559 --> 00:38:18.239
a best guess based on all kinds of factors. There's there's,

655
00:38:18.639 --> 00:38:23.000
there's there, there's potential error and unpredictable aspects. Uh, you know,

656
00:38:23.079 --> 00:38:25.480
riddle through all of this that that that are going

657
00:38:25.519 --> 00:38:27.840
on at a greater rate than ever. So that's the

658
00:38:27.840 --> 00:38:29.920
whole purpose of the book Random Acts, which is available

659
00:38:29.960 --> 00:38:34.119
on Amazon. Uh, it's uh, you know, the publisher is

660
00:38:34.119 --> 00:38:36.360
called post Hill, which is a great publisher out of

661
00:38:37.280 --> 00:38:41.599
out of National, Tennessee. And uh, happy to finally get

662
00:38:41.599 --> 00:38:41.920
it done.

663
00:38:43.280 --> 00:38:46.320
Well, congratulations and sounds like it's going to help a

664
00:38:46.400 --> 00:38:49.639
lot of people. So I'm sure that you know, our

665
00:38:49.679 --> 00:38:52.159
audience is going to definitely go and get a copy

666
00:38:52.199 --> 00:38:53.920
of it. And if they want to connect with you,

667
00:38:54.039 --> 00:38:55.679
what's the best way to get in.

668
00:38:55.639 --> 00:39:00.599
Touch with Well, I'm all over LinkedIn, but you know,

669
00:39:00.719 --> 00:39:03.280
all throw out my email if you want to contact me,

670
00:39:03.760 --> 00:39:08.960
just see Leclair That's l. E. C. L A R

671
00:39:09.000 --> 00:39:10.239
at Forester dot com.

672
00:39:11.440 --> 00:39:11.960
Fantastic.

673
00:39:12.760 --> 00:39:14.360
Yeah.

674
00:39:14.840 --> 00:39:18.000
Well, thank you for joining us, Craig and sharing your

675
00:39:18.000 --> 00:39:22.960
insights and giving us different perspectives that how we can

676
00:39:23.119 --> 00:39:25.400
go forward, how we can approach it, and how we

677
00:39:25.440 --> 00:39:29.679
can kind of continue working with it and growing with it.

678
00:39:30.519 --> 00:39:32.159
So thank you for joining.

679
00:39:32.039 --> 00:39:34.880
Us and thank you for having me.

680
00:39:35.599 --> 00:39:39.199
Absolutely thank you to your audience for joining us, because

681
00:39:39.239 --> 00:39:42.719
without you, the show would not be possible. You are

682
00:39:42.840 --> 00:39:45.239
the life and soul of our show. Reach out to us,

683
00:39:45.320 --> 00:39:48.400
let us know how we can support you and help

684
00:39:48.480 --> 00:39:52.800
you live the life you deserve and achieve your goals.

685
00:39:53.119 --> 00:39:55.639
And thank you one for making the show technically possible.

686
00:39:56.880 --> 00:39:59.280
Thank you for being part of Beyond Confidence. With your

687
00:39:59.280 --> 00:40:01.599
host dev part Are, we hope you have learned more

688
00:40:01.639 --> 00:40:04.320
about how to start living the life you want. Each

689
00:40:04.360 --> 00:40:07.280
week on Beyond Confidence, you hear stories of real people

690
00:40:07.360 --> 00:40:11.840
who've experienced growth by overcoming their fears and building meaningful relationships.

691
00:40:12.199 --> 00:40:15.599
During Beyond Confidence, Dvpark shares what happened to her when

692
00:40:15.639 --> 00:40:18.039
she stepped out of her comfort zone to work directly

693
00:40:18.079 --> 00:40:21.159
with people across the globe. She not only coaches people

694
00:40:21.159 --> 00:40:24.679
how to form hard connections, but also transform relationships to

695
00:40:24.760 --> 00:40:28.159
mutually beneficial partnerships. As they strive to live the life

696
00:40:28.159 --> 00:40:30.480
they want. If you are ready to live the life

697
00:40:30.559 --> 00:40:34.280
you want and leverage your strengths, learn more at www

698
00:40:34.360 --> 00:40:38.239
dot dvpark dot com and you can connect with dvat

699
00:40:38.280 --> 00:40:41.800
contact at dvpark dot com. We look forward to you

700
00:40:41.960 --> 00:40:43.039
joining us next week